We use the Adversarial Auto-Encoder [1]} as a basic structure and for some part of the network refer to the DCGAN [2]} for better performance.
However, any Auto-encoder structure can be used instead.
Images are transformed into 64x64 size RGB 3 channel and fed to network as input.
Every training was done with 1000 epoch with batch size 1024.
Adam is used for an optimizer and we used the same parameter as paper[3]}.
Batch normalization[4]} is applied after convolution layer and we used ReLU as an activation function for both encoder and decoder.
However, only the last layer of the decoder, we used tanh exceptionally[2]}.
With adversarial part of AAE, we simply impose gaussian distribution to the latent code[1]} for simplicity.
